1. Field of the Invention
The present invention is directed to a method and to an arrangement for the recognition of spoken language by a computer.
2. Description of the Related Art
A method and an arrangement for the recognition of spoken language are known from publication, by N. Haberland et al., xe2x80x9cSprachunterrichtxe2x80x94wie funktioniert die computerbasierte Spracherkennung!xe2x80x9d, c""txe2x80x94Magazin fxc3xcr Computertechnikxe2x80x94May 1998, Heinz Heise Verlag, Hannover, 1998. Particularly until a recognized word sequence is obtained from a digitalized voice signal, a signal analysis and a global search that accesses an acoustic model and a linguistic model of the language to be recognized are implemented in the recognition of spoken language. The acoustic model is based on a phoneme inventory, converted with the assistance of hidden Markov models (HMMs), and on a pronunciation lexicon, converted as a tree lexicon. The linguistic model contains a tri-gram statistics, i.e. a sequence of three words. With the assistance of the acoustic model, the most probable word sequences are determined during the global search for feature vectors that proceeded from the signal analysis and these are output as recognized word sequence. The relationship that has been presented is explained in depth in publication by N. Haberland et al., Sprachunterrichtxe2x80x94wie funktioniert die computerbasierte Spracherkennung.
In order to follow the subsequent comments, the terms that are employed shall be briefly discussed here.
As a phase of the computer-based speech recognition, the signal analysis particularly comprises a Fourier transformation of the digitalized voice signal and a feature extraction following thereupon. It proceeds from publication by N. Haberland et al., xe2x80x9cSprachunterrichtxe2x80x94wie funktioniert die computerbasierte Spracherkennung?xe2x80x9d that the signal analysis ensues every ten milliseconds. From overlapping time segments with a respective duration of, for example, 25 milliseconds, approximately 30 features are determined on the basis of the signal analysis and combined to form a feature vector. For example, given a sampling frequency of 16 kHz, 400 signal amplitude values enter into the calculation of a feature vector. In particular, the components of the feature vector describe the spectral energy distribution of the appertaining signal excerpt. In order to arrive at this energy distribution, a Fourier transformation is implemented on every signal excerpt (25 ms excerpt). The presentation of the signal in the frequency domain is thus obtained and, thus, the components of the feature vector. After the signal analysis, thus, the digitalized voice signal is present in the form of feature vectors.
These feature vectors are supplied to the global search, a further phase of the speech recognition. As already mentioned, the global search makes use of the acoustic model and, potentially, of the linguistic model in order to image the sequence of feature vectors onto individual parts of the language (vocabulary) which are present as a model A language is composed of a given plurality of sounds, that are referred to as a phonemes, whose totality is referred to as phoneme inventory. The vocabulary is modelled by phoneme sequences and stored in a pronunciation lexicon. Each phoneme is modelled by at least one HMM. A plurality of HMMs yield a stochastic automaton that comprises statusses and status transitions. The time execution of the occurrence of specific feature vectors (even within a phoneme) can be modelled with HMMs. A corresponding phoneme model thereby comprises a given plurality of statusses that are arranged in linear succession. A status of an HMM represents a part of a phoneme (for example an excerpt of 10 ms length). Each status is linked to an emission probability, which, in particular, has a Gaussian distribution, for the feature vectors and to transition probabilities for the possible transitions. A probability with which a feature vector is observed in an appertaining status is allocated to the feature vector with the emission distribution. The possible transitions are a direct transition from one status into a next status, a repetition of the status and a skipping of the status.
The joining of the HMM statusses to the appertaining transitions over the time is referred to as a trellis. The principle of dynamic programming is employed in order to determined the acoustic probability of a word: the path through the trellis is sought that exhibits the fewest errors or, respectively, that is defined by the highest probability for a word to be recognized.
Parameters of the emission distributions are determined on the basis of exemplary sets in a training phase.
In addition to the described acoustic model, the language model (also: linguistic model) is also potentially taken into consideration in the global search. The language model has the job of determining the linguistic probability of a set hypothesis. When a sequence of words has no meaning, then this sequence has a correspondingly slight linguistic probability in the language model. In particular, sequences of two words (bi-grams) or of three words (tri-grams) are utilized in order to calculate linguistic probabilities for these bi-grams or, respectively, tri-grams. Due to the nearly arbitrary combination of words of the vocabulary in bi-grams, tri-grams or, respectively, n-grams, a storing of all n-grams is ultimately a question of the available memory.
The result of the global search is the output or, respectively, offering of a recognized word sequence that derives taking the acoustic model (phoneme inventory) and the language model into consideration.
Given an HMM, it is assumed that an emission probability for a feature vector is dependent on only one status. Modelling errors that, according to the above comments, have a significant influence on the recognized word sequence derive as a result of this assumption.
The publication Kenny et al., xe2x80x9cLinear Predictive HMM for Vector-Valued Observations with Applications to Speech recognitionxe2x80x9d, IEEE Transactions on ASSP, Volume 38, 1990, pages 220-225, discloses a method for recognizing spoken language with a computer wherein feature vectors for describing a digitalized voice signal are calculated dependent of a plurality of preceding feature vectors.
An object of the present invention is to provide an arrangement and a method for speech recognition that enables an improved recognition on the basis of modified hidden Markov models compared to the Prior Art.
This object is achieved a method for recognizing spoken language with a computer, wherein a digitalized voice signal is determined from the spoken language, a signal analysis from which feature vectors for describing the digitalized voice signal proceed is implemented on the digitalized voice signal, a global search for imaging the feature vectors onto a language present in modelled form is implemented, whereby phonemes of the language are described by a modified hidden Markov model, the modified hidden Markov model comprises a conditional probability of a feature vector of a prescribed plurality of prescribed plurality of preceding feature vectors, the conditional probability is approximated by a combination of two separately modelled probabilities, the first separately modelled probability ignores a correlation of the feature vectors, whereas the second separately modelled probability takes the correlation of the feature vectors into consideration, and the spoken language is recognized in that a recognized word sequence is offered by the global search.
For achieving the object, a method for the recognition of spoken language with a computer is recited wherein a digitalized speech signal is determined from the spoken language and a signal analysis is implemented with the digitalized voice signal, whereby feature vectors are determined for the description of the digitalized voice signal. In a global search for imaging the feature vectors on to a language present in modelled form, each phoneme of the language is described by a modified hidden Markov model. The modified hidden Markov model thereby comprises a conditional probability of a feature vector dependent on a pre-determined plurality of preceding feature vectors. The spoken language is recognized in that a recognized word sequence is offered as result of the global search.
It is thereby a decided advantage that a clearly better recognition rate can be documented compared to the modelling of the hidden Markov model known from the Prior Art (see the introduction to the specification) on the basis of the description of the modified hidden Markov model by taking a correlation of preceding feature vectors into consideration.
One development of the invention is comprised therein that the modified hidden Markov model additionally comprises a conditional probability of each status.
A clear improvement of the speech recognition arises by taking past feature vectors and the respective HMM status into consideration.
In particular, the method serves for the recognition of key words in spoken language. It is important in the key word recognition that all key words are dependably recognized from a pre-determined, closed set of key words). Given employment of a speech recognition system for example individual word recognition in the framework of a safety-critical application, for example in the control of a motor vehicle, it is important that the spoken commands are dependably recognized, i.e. with little error, and, thus, an emergency control to be potentially promoted is assured by voice input.
The method can also be utilized in automatic computer-assisted dialoging.
In dialoging, the vocabulary is preferably limited to a specific field of application, for example banking, hotel reservations or ordering a rental car. Such a system must assure high user acceptance since, when a user repeatedly does not understand, there is the risk that this user will no longer use the system. In particular, the relevant words are thereby recognized in the spoken language, whereby this can be expressed fluently and independently of the speaker.
The use of the method presented here is advantageous both in the automatic recognition of key words as well as in computer-assisted dialoging, since an improved recognition performance respectively promotes user acceptance and, thus, promotes the spread of computer-based speech recognition systems.
Further, an arrangement for the recognition of spoken language is recited that comprises a unit for signal analysis that is configured such that feature vectors can be determined from the spoken language. Further, a unit for the global search is provided that is configured such that the feature vectors can be imaged onto a language present in modelled form. Finally, the arrangement comprises a processor unit that is configured such that a modified hidden Markov model comprises a conditional probability for a current feature vector dependent on a predetermined plurality of preceding feature vectors, whereby an imaging of the feature vectors onto the language present in modelled form can be implemented with the modified hidden Markov model.
One development of the invention is comprised therein that the processor unit is configured such that the modified hidden Markov model additionally comprises a conditional probability of a respectively current status.
This arrangement/apparatus is especially suited for the implementation of the above-explained inventive method or one of its developments.
Developments of the invention are provided by improvements, for example, wherein the modified hidden Markov model additionally comprises a conditional probability of a respectively current status. The method may be used for recognizing key words in the spoken language. In one embodiment, the method provides automatic, computer-assisted dialoging.
The present invention also provides an arrangement for recognizing spoken language, including a unit for signal analysis that is configured such that feature vectors can be determined from the spoken language, a unit for global searching that is configured such that the feature vectors can be imaged onto a language present in modelled form, a processor unit that is configured such that a modified hidden Markov model comprises a conditional probability for a current feature vector dependent on a predetermined plurality of preceding feature vectors, whereby the conditional probability is approximated by a combination of two separately modelled probabilities, the first separately modelled probability ignoring a correlation of the feature vectors, whereas the second separately modelled probability takes the correlation of the feature vectors into consideration, and an imaging of the feature vectors onto the language present in modelled form can be implemented with the modified hidden Markov model.
In a preferred embodiment, the arrangement whereby the processor unit is configured such that the modified hidden Markov model additionally comprises a conditional probability of a respectively current status.